Learning admittance mappings for force-guided assembly

We present a practical method for autonomous synthesis of appropriate admittance behavior for robust high-precision robotic assembly. Because our approach relies on online learning of the appropriate admittance through repeated attempts at the assembly operation, we are able to circumvent the problems alternative approaches have in trying to model the interactions between the robot and its environment. Test results on the peg-in-hole insertion task show that the performance of our approach compares favorably with that of other methods recently proposed for high-precision chamferless peg-in-hole insertion